Relationships between migration rates and landscape resistance assessed using individual-based simulations

Linking landscape effects on gene flow to processes such as dispersal and mating is essential to provide a conceptual foundation for landscape genetics. It is particularly important to determine how classical population genetic models relate to recent individual-based landscape genetic models when assessing individual movement and its influence on population genetic structure. We used classical Wright-Fisher models and spatially explicit, individual-based, landscape genetic models to simulate gene flow via dispersal and mating in a series of landscapes representing two patches of habitat separated by a barrier. We developed a mathematical formula that predicts the relationship between barrier strength (i.e., permeability) and the migration rate (m) across the barrier, thereby linking spatially explicit landscape genetics to classical population genetics theory. We then assessed the reliability of the function by obtaining population genetics parameters (m, F(ST) ) using simulations for both spatially explicit and Wright-Fisher simulation models for a range of gene flow rates. Next, we show that relaxing some of the assumptions of the Wright-Fisher model can substantially change population substructure (i.e., F(ST) ). For example, isolation by distance among individuals on each side of a barrier maintains an F(ST) of ～0.20 regardless of migration rate across the barrier, whereas panmixia on each side of the barrier results in an F(ST) that changes with m as predicted by classical population genetics theory. We suggest that individual-based, spatially explicit modelling provides a general framework to investigate how interactions between movement and landscape resistance drive population genetic patterns and connectivity across complex landscapes.     

Genetic covariances within and between species: indirect selection for hybrid inviability

Genotype-environment interactions and natural selection can result in local specialization when different genotypes are favored in different environments. Restricted gene flow or genetic subdivision enhances local genetic diversification across a species when natural selection acts on such variation. The indirect evolution of reproductive isolation and the restriction of gene flow between species in statu nascendi may provide a central role for genotype-environment interactions in speciation genetics. We derive the expected genetic covariance between heterospecific and conspecific viability fitness under several different models of selection, dominance, and breeding structure. Standard quantitative genetic methods can be used to estimate these covariances in experimental studies. These genetic covariances permit us to evaluate in a formal way the indirect effects of selection within a species on the evolution of hybrid inviability between species. We find that, for autosomal loci and random mating, the genetic covariance across species is equal to the product of three quantities: (1) the viability of the best hybrid genotype; (2) the viability effect of an allele in hybrids; and, (3) the change in allele frequency due to selection in the conspecific population. Inbreeding within the conspecific population, expressed as Wright's coefficient, F, increases the genetic covariance by a factor (1 + F). In all cases, a negative genetic covariance across species is evidence for hybrid inviability evolving as an indirect effect of selection within species for adaptive (as opposed to neutral) genetic change. “It is an irony of evolutionary genetics, that although it is a fusion of Mendelism and Darwinism, it has made no direct contribution to what Darwin obviously saw as the fundamental problem: the origin of species…. While it is a question of elementary population genetics to state how many generations will be required for the frequency of an allele to change from q₁ to q₂, we do not know how to incorporate such a statement into speciation theory, in large part because we know virtually nothing about the genetic changes that occur in species formation.” (Lewontin 1974, p. 159)     

cdpop: A spatially explicit cost distance population genetics program

Spatially explicit simulation of gene flow in complex landscapes is essential to explain observed population responses and provide a foundation for landscape genetics. To address this need, we wrote a spatially explicit, individual-based population genetics model (cdpop). The model implements individual-based population modelling with Mendelian inheritance and k-allele mutation on a resistant landscape. The model simulates changes in population and genotypes through time as functions of individual based movement, reproduction, mortality and dispersal on a continuous cost surface. This model will be a valuable tool for the study of landscape genetics by increasing our understanding about the effects of life history, vagility and differential models of landscape resistance on the genetic structure of populations in complex landscapes.     

Effects of the landscape on boreal toad gene flow: does the pattern–process relationship hold true across distinct landscapes at the northern range margin?

Understanding the impact of natural and anthropogenic landscape features on population connectivity is a major goal in evolutionary ecology and conservation. Discovery of dispersal barriers is important for predicting population responses to landscape and environmental changes, particularly for populations at geographic range margins. We used a landscape genetics approach to quantify the effects of landscape features on gene flow and connectivity of boreal toad (Bufo boreas) populations from two distinct landscapes in south-east Alaska (Admiralty Island, ANM, and the Chilkat River Valley, CRV). We used two common methodologies for calculating resistance distances in landscape genetics studies (resistance based on least-cost paths and circuit theory). We found a strong effect of saltwater on genetic distance of CRV populations, but no landscape effects were found for the ANM populations. Our discordant results show the importance of examining multiple landscapes that differ in the variability of their features, to maximize detectability of underlying processes and allow results to be broadly applicable across regions. Saltwater serves as a physiological barrier to boreal toad gene flow and affects populations on a small geographic scale, yet there appear to be few other barriers to toad dispersal in this intact northern region.     

Sample design effects in landscape genetics

An important research gap in landscape genetics is the impact of different field sampling designs on the ability to detect the effects of landscape pattern on gene flow. We evaluated how five different sampling regimes (random, linear, systematic, cluster, and single study site) affected the probability of correctly identifying the generating landscape process of population structure. Sampling regimes were chosen to represent a suite of designs common in field studies. We used genetic data generated from a spatially-explicit, individual-based program and simulated gene flow in a continuous population across a landscape with gradual spatial changes in resistance to movement. Additionally, we evaluated the sampling regimes using realistic and obtainable number of loci (10 and 20), number of alleles per locus (5 and 10), number of individuals sampled (10–300), and generational time after the landscape was introduced (20 and 400). For a simulated continuously distributed species, we found that random, linear, and systematic sampling regimes performed well with high sample sizes (>200), levels of polymorphism (10 alleles per locus), and number of molecular markers (20). The cluster and single study site sampling regimes were not able to correctly identify the generating process under any conditions and thus, are not advisable strategies for scenarios similar to our simulations. Our research emphasizes the importance of sampling data at ecologically appropriate spatial and temporal scales and suggests careful consideration for sampling near landscape components that are likely to most influence the genetic structure of the species. In addition, simulating sampling designs a priori could help guide filed data collection efforts     

On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system

Dispersal and natural selection are key evolutionary processes shaping the distribution of phenotypic and genetic diversity. For species inhabiting complex spatial environments however, it is unclear how the balance between gene flow and selection may be influenced by landscape heterogeneity and environmental variation. Here, we evaluated the effects of dendritic landscape structure and the selective forces of hydroclimatic variation on population genomic parameters for the Murray River rainbowfish, Melanotaenia fluviatilis across the Murray–Darling Basin, Australia. We genotyped 249 rainbowfish at 17,503 high‐quality SNP loci and integrated these with models of network connectivity and high‐resolution environmental data within a riverscape genomics framework. We tested competing models of gene flow before using multivariate genotype–environment association (GEA) analysis to test for signals of adaptive divergence associated with hydroclimatic variation. Patterns of neutral genetic variation were consistent with expectations based on the stream hierarchy model and M. fluviatilis’ moderate dispersal ability. Models incorporating dendritic network structure suggested that landscape heterogeneity is a more important factor determining connectivity and gene flow than waterway distance. Extending these results, we also introduce a novel approach to controlling for the unique effects of dendritic network structure in GEA analyses of populations of aquatic species. We identified 146 candidate loci potentially underlying a polygenic adaptive response to seasonal fluctuations in stream flow and variation in the relative timing of temperature and precipitation extremes. Our findings underscore an emerging predominant role for seasonal variation in hydroclimatic conditions driving local adaptation and are relevant for informing proactive conservation management.     

Landscape genomic signatures indicate reduced gene flow and forest‐associated adaptive divergence in an endangered neotropical turtle

Human‐induced transformations of ecosystems usually result in fragmented populations subject to increased extinction risk. Fragmentation is also often associated with novel environmental heterogeneity, which in combination with restricted gene flow may increase the opportunity for local adaptation. To manage at‐risk populations in these landscapes, it is important to understand how gene flow is changing, and how populations respond to habitat loss. We conducted a landscape genomics analysis using Restriction‐site Associated DNA sequencing to investigate the evolutionary response of the critically endangered Dahl's Toad‐headed turtle (Mesoclemmys dahli) to severe habitat modification. The species has lost almost all of its natural habitat in the southwestern part of its range and about 70% in the northeast. Based on least cost path analysis across different resistance surfaces for 3,211 SNPs, we found that the landscape matrix is restricting gene flow, causing the fragmentation of the species into at least six populations. Genome scans and allele‐environment association analyses indicate that the population fragments in the deforested grasslands of the southwest are adaptively different from those in the more forested northeast. Populations in areas with no forest had low levels of adaptive genetic diversity and the fixation of ancestrally‐polymorphic SNPs, consistent with directional selection in this novel environment. Our results suggest that this forest‐stream specialist is adapting to pond‐grassland conditions, but it is also suffering from negative consequences of habitat loss, including genetic erosion, isolation, small effective population sizes, and inbreeding. We recommend gene flow restoration via genetic rescue to counteract these threats, and provide guidance for this strategy.     

Spatial processes and evolutionary models: a critical review

Evolution is a fundamentally population level process in which variation, drift and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time using models like Brownian motion, stabilizing selection (Ornstein–Uhlenbeck) and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow and geographical range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local‐population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long‐term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population‐level notions from these standard macroevolutionary models.     

RIVERSCAPE GENETICS IDENTIFIES REPLICATED ECOLOGICAL DIVERGENCE ACROSS AN AMAZONIAN ECOTONE

Ecological speciation involves the evolution of reproductive isolation and niche divergence in the absence of a physical barrier to gene flow. The process is one of the most controversial topics of the speciation debate, particularly in tropical regions. Here, we investigate ecologically based divergence across an Amazonian ecotone in the electric fish, Steatogenys elegans. We combine phylogenetics, genome scans, and population genetics with a recently developed individual‐based evolutionary landscape genetics approach that incorporates selection. This framework is used to assess the relative contributions of geography and divergent natural selection between environments as biodiversity drivers. We report on two closely related and sympatric lineages that exemplify how divergent selection across a major Amazonian aquatic ecotone (i.e., between rivers with markedly different hydrochemical properties) may result in replicated ecologically mediated speciation. The results link selection across an ecological gradient with reproductive isolation and we propose that assortative mating based on water color may be driving the divergence. Divergence resulting from ecologically driven selection highlights the importance of considering environmental heterogeneity in studies of speciation in tropical regions. Furthermore, we show that framing ecological speciation in a spatially explicit evolutionary landscape genetics framework provides an important first step in exploring a wide range of the potential effects of spatial dependence in natural selection.     

A genomic selection component analysis characterizes migration‐selection balance

The genetic differentiation of populations in response to local selection pressures has long been studied by evolutionary biologists, but key details about the process remain obscure. How rapidly can local adaptation evolve, how extensive is the process across the genome, and how strong are the opposing forces of natural selection and gene flow? Here, we combine direct measurement of survival and reproduction with whole‐genome genotyping of a plant species (Mimulus guttatus) that has recently invaded a novel habitat (the Quarry population). We renovate the classic selection component method to accommodate genomic data and observe selection at SNPs throughout the genome. SNPs showing viability selection in Quarry exhibit elevated divergence from neighboring populations relative to neutral SNPs. We also find that nonsignificant SNPs exhibit a subtle, but still significant, change in allele frequency toward neighboring populations, a predicted effect of gene flow. Given that the Quarry population is most probably only 30–40 generations old, the alleles conferring local advantage are almost certainly older than the population itself. Thus, local adaptation owes to the recruitment of standing genetic variation.     

Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti

Knowledge of dispersal-related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information-theoretic model selection and multi-model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces.     

Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment

Disentangling the relative contributions of selective and neutral processes underlying phenotypic and genetic variation under natural, environmental conditions remains a central challenge in evolutionary ecology. However, much of the variation that could be informative in this area of research is likely to be cryptic in nature; thus, the identification of wild populations suitable for study may be problematic. We use a landscape genetics approach to identify such populations of three-spined stickleback inhabiting the Saint Lawrence River estuary. We sampled 1865 adult fish over multiple years. Individuals were genotyped for nine microsatellite loci, and georeferenced multilocus data were used to infer population groupings, as well as locations of genetic discontinuities, under a Bayesian model framework ( geneland ). We modelled environmental data using nonparametric multiple regression to explain genetic differentiation as a function of spatio-ecological effects. Additionally, we used genotype data to estimate dispersal and gene flow to parameterize a simple model predicting adaptive vs. plastic divergence between demes. We demonstrate a bipartite division of the genetic landscape into freshwater and maritime zones, independent of geographical distance. Moreover, we show that the greatest proportion of genetic variation (31.5%) is explained by environmental differences. However, the potential for either adaptive or plastic divergence between demes is highly dependent upon the strength of migration and selection. Consequently, we highlight the utility of landscape genetics as a tool for hypothesis generation and experimental design, to identify focal populations and putative selection gradients, in order to distinguish between phenotypic plasticity and local adaptation.     

Modelling multilocus selection in an individual‐based,spatially‐explicit landscape genetics framework

We implemented multilocus selection in a spatially‐explicit, individual‐based framework that enables multivariate environmental gradients to drive selection in many loci as a new module for the landscape genetics programs, CDPOP and CDMetaPOP. Our module simulates multilocus selection using a linear additive model, providing a flexible platform to evaluate a wide range of genotype‐environment associations. Importantly, the module allows simulation of selection in any number of loci under the influence of any number of environmental variables. We validated the module with individual‐based selection simulations under Wright‐Fisher assumptions. We then evaluated results for simulations under a simple landscape selection model. Next, we simulated individual‐based multilocus selection across a complex selection landscape with three loci linked to three different environmental variables. Finally, we demonstrated how the program can be used to simulate multilocus selection under varying selection strengths across different levels of gene flow in a landscape genetics framework. This new module provides a valuable addition to the study of landscape genetics, allowing for explicit evaluation of the contributions and interactions between gene flow and selection‐driven processes across complex, multivariate environmental and landscape conditions.     

Sun skink landscape genomics: assessing the roles of micro‐evolutionary processes in shaping genetic and phenotypic diversity across a heterogeneous and fragmented landscape

Incorporating genomic data sets into landscape genetic analyses allows for powerful insights into population genetics, explicitly geographical correlates of selection, and morphological diversification of organisms across the geographical template. Here, we utilize an integrative approach to examine gene flow and detect selection, and we relate these processes to genetic and phenotypic population differentiation across South‐East Asia in the common sun skink, Eutropis multifasciata. We quantify the relative effects of geographic and ecological isolation in this system and find elevated genetic differentiation between populations from island archipelagos compared to those on the adjacent South‐East Asian continent, which is consistent with expectations concerning landscape fragmentation in island archipelagos. We also identify a pattern of isolation by distance, but find no substantial effect of ecological/environmental variables on genetic differentiation. To assess whether morphological conservatism in skinks may result from stabilizing selection on morphological traits, we perform F_ST–P_ST comparisons, but observe that results are highly dependent on the method of comparison. Taken together, this work provides novel insights into the manner by which micro‐evolutionary processes may impact macro‐evolutionary scale biodiversity patterns across diverse landscapes, and provide genomewide confirmation of classic predictions from biogeographical and landscape ecological theory.     

Landscape characteristics influencing the genetic structure of greater sage‐grouse within the stronghold of their range: a holistic modeling approach

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high‐ and low‐quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage‐grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low‐quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33‐km‐diameter moving windows were preferred, suggesting small‐scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.     

Evaluating sample allocation and effort in detecting population differentiation for discrete and continuously distributed individuals

One of the most pressing issues in spatial genetics concerns sampling. Traditionally, substructure and gene flow are estimated for individuals sampled within discrete populations. Because many species may be continuously distributed across a landscape without discrete boundaries, understanding sampling issues becomes paramount. Given large-scale, geographically broad conservation efforts, researchers are looking for guidance as to the trade-offs between sampling more individuals within a population versus few individuals scattered across more populations. Here, we conducted simulations that address these issues. We first established two archetypical patterns of dispersion: (1) individuals within discrete populations, and (2) continuously distributed individuals with limited dispersal. We used genotypes generated from a spatially-explicit, individual-based program and simulated genetic structure in individuals from nine different population sizes across a landscape that either had barriers to movement (defining discrete populations) or isolation-by-distance patterns (defining continuously distributed individuals). Then, given each pattern of dispersion, we allocated samples across four different sampling strategies for each of the nine population sizes in various configurations for sampling more individuals within a population versus fewer individuals scattered across more populations. We assessed the population genetic substructure with both the population-based metric, F _ST, and an individual-based metric, D _PS regardless of the true pattern of dispersion to allow us to better understand the effect of incorrectly matching the metric and the distribution (e.g., F _ST with continuously distributed individuals, and vice versa). We show that sampling many subpopulations (or sampling areas), thus sampling fewer individuals per subpopulation, overestimates measures of population subdivision with the population-based metric for both patterns of dispersion. In contrast, using the individual-based metric gives the opposite results: sampling too few subpopulations, and many individuals per subpopulation, produces an underestimate of the strength of isolation-by-distance. By comparing all results, we were able to suggest a strong predictive model of a chosen genetic structure metric for elucidating the sampling design trade-offs given each pattern of dispersion and configuration on the landscape.     

Simulating plasticity as a framework for understanding habitat selection and its role in adaptive capacity and extinction risk through an expansion of CDMetaPOP

Adaptive capacity can present challenges for modelling as it encompasses multiple ecological and evolutionary processes such as natural selection, genetic drift, gene flow and phenotypic plasticity. Spatially explicit, individual-based models provide an outlet for simulating these complex interacting eco-evolutionary processes. We expanded the existing Cost-Distance Meta-POPulation (CDMetaPOP) framework with inducible plasticity modelled as a habitat selection behaviour, using temperature or habitat quality variables, with a genetically based selection threshold conditioned on past individual experience. To demonstrate expected results in the new module, we simulated hypothetical populations and then evaluated model performance in populations of redband trout (Oncorhynchus mykiss gairdneri) across three watersheds where temperatures induce physiological stress in parts of the stream network. We ran simulations using projected warming stream temperature data under four scenarios for alleles that: (1) confer thermal tolerance, (2) bestow plastic habitat selection, (3) give both thermal tolerance and habitat selection preference and (4) do not provide either thermal tolerance or habitat selection. Inclusion of an adaptive allele decreased declines in population sizes, but this impact was greatly reduced in the relatively cool stream networks. As anticipated with the new module, high-temperature patches remained unoccupied by individuals with the allele operating plastically after exposure to warm temperatures. Using complete habitat avoidance above the stressful temperature threshold, habitat selection reduced the overall population size due to the opportunity cost of avoiding areas with increased, but not guaranteed, mortality. Inclusion of plasticity within CDMetaPOP will provide the potential for genetic or plastic traits and ‘rescue’ to affect eco-evolutionary dynamics for research questions and conservation applications.     

Islands of water in a sea of dry land: hydrological regime predicts genetic diversity and dispersal in a widespread fish from Australia's arid zone, the golden perch (Macquaria ambigua)

Rivers provide an excellent system to study interactions between patterns of biodiversity structure and ecological processes. In these environments, gene flow is restricted by the spatial hierarchy and temporal variation of connectivity within the drainage network. In the Australian arid zone, this variability is high and rivers often exist as isolated waterholes connected during unpredictable floods. These conditions cause boom/bust cycles in the population dynamics of taxa, but their influence on spatial genetic diversity is largely unknown. We used a landscape genetics approach to assess the effect of hydrological variability on gene flow, spatial population structure and genetic diversity in an Australian freshwater fish, Macquaria ambigua. Our analysis is based on microsatellite data of 590 samples from 26 locations across the species range. Despite temporal isolation of populations, the species showed surprisingly high rates of dispersal, with population genetic structure only evident among major drainage basins. Within drainages, hydrological variability was a strong predictor of genetic diversity, being positively correlated with spring-time flow volume. We propose that increases in flow volume during spring stimulate recruitment booms and dispersal, boosting population size and genetic diversity. Although it is uncertain how the hydrological regime in arid Australia may change under future climate scenarios, management strategies for arid-zone fishes should mitigate barriers to dispersal and alterations to the natural flow regime to maintain connectivity and the species' evolutionary potential. This study contributes to our understanding of the influence of spatial and temporal heterogeneity on population and landscape processes.     

Landscape genetics of plants

Landscape genetics is the amalgamation of landscape ecology and population genetics to help with understanding microevolutionary processes such as gene flow and adaptation. In this review, we examine why landscape genetics of plants lags behind that of animals, both in number of studies and consideration of landscape elements. The classical landscape distance/resistance approach to study gene flow is challenging in plants, whereas boundary detection and the assessment of contemporary gene flow are more feasible. By contrast, the new field of landscape genetics of adaptive genetic variation, establishing the relationship between adaptive genomic regions and environmental factors in natural populations, is prominent in plant studies. Landscape genetics is ideally suited to study processes such as migration and adaptation under global change.     

Modelling single nucleotide effects in phosphoglucose isomerase on dispersal in the Glanville fritillary butterfly: coupling of ecological and evolutionary dynamics

Dispersal comprises a complex life-history syndrome that influences the demographic dynamics of especially those species that live in fragmented landscapes, the structure of which may in turn be expected to impose selection on dispersal. We have constructed an individual-based evolutionary sexual model of dispersal for species occurring as metapopulations in habitat patch networks. The model assumes correlated random walk dispersal with edge-mediated behaviour (habitat selection) and spatially correlated stochastic local dynamics. The model is parametrized with extensive data for the Glanville fritillary butterfly. Based on empirical results for a single nucleotide polymorphism (SNP) in the phosphoglucose isomerase (Pgi) gene, we assume that dispersal rate in the landscape matrix, fecundity and survival are affected by a locus with two alleles, A and C, individuals with the C allele being more mobile. The model was successfully tested with two independent empirical datasets on spatial variation in Pgi allele frequency. First, at the level of local populations, the frequency of the C allele is the highest in newly established isolated populations and the lowest in old isolated populations. Second, at the level of sub-networks with dissimilar numbers and connectivities of patches, the frequency of C increases with decreasing network size and hence with decreasing average metapopulation size. The frequency of C is the highest in landscapes where local extinction risk is high and where there are abundant opportunities to establish new populations. Our results indicate that the strength of the coupling of the ecological and evolutionary dynamics depends on the spatial scale and is asymmetric, demographic dynamics having a greater immediate impact on genetic dynamics than vice versa.